A framework for statistical clustering with constant time approximation algorithms for K-median and K-means clustering

No abstract is available for this paper.

• Publication year

  2007

• Venue

  Machine-mediated learning

• Publication date

  2007-03-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1007/s10994-006-0587-3
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A k-Median Algorithm with Running Time Independent of Data Size

  2004influential reference
• A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering

  2004influential reference
• Sublinear-Time Approximation for Clustering Via Random Sampling

  2004influential reference
• Approximation schemes for clustering problems

  2003influential reference
• Optimal Time Bounds for Approximate Clustering

  2002cited by this paper
• Sublinear time approximate clustering

  2001influential reference
• Neural Network Learning: Theoretical Foundations

  1999cited by this paper
• Learning in Neural Networks: Theoretical Foundations

  1999cited by this paper
• Quantization Functionals and Regularized Principal Manifolds

  1998cited by this paper
• Empirical Risk Approximation: An Induction Principle for Unsupervised Learning

  1998cited by this paper
• The minimax distortion redundancy in empirical quantizer design

  1997cited by this paper
• Quantization and the method of k -means

  1982cited by this paper

• Topological K-means clustering in reproducing kernel Hilbert spaces

  2025cites this paper
• Settling Time vs. Accuracy Tradeoffs for Clustering Big Data

  2024cites this paper
• Replicable Clustering

  2023cites this paper
• A Constant-Factor Approximation Algorithm for Reconciliation k-Median

  2023influential citation
• The Power of Uniform Sampling for k-Median

  2023cites this paper
• A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

  2022cites this paper
• Convergence of online k-means

  2022cites this paper
• Stratification to Improve Systematic Sampling for Big Data Mining Using Approximate Clustering

  2021cites this paper
• Uniform and Non-uniform Sampling Methods for Sub-linear Time k-means Clustering

  2021cites this paper
• Approximate Partitional Clustering Through Systematic Sampling in Big Data Mining

  2021cites this paper
• Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces

  2021cites this paper
• Euclidean distance stratified random sampling based clustering model for big data mining

  2021cites this paper
• Uniform Concentration Bounds toward a Unified Framework for Robust Clustering

  2021cites this paper
• Stratified linear systematic sampling based clustering approach for detection of financial risk group by mining of big data

  2021cites this paper
• A novel facility location problem for taxi hailing platforms

  2020cites this paper
• Sequential no-Substitution k-Median-Clustering

  2019influential citation
• Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data

  2019cites this paper
• Unexpected Effects of Online K-means Clustering

  2019cites this paper
• L G ] 3 0 M ay 2 01 9 Sequential no-Substitution k-Median-Clustering

  2019influential citation
• A numerical measure of the instability of Mapper-type algorithms

  2019cites this paper
• Randomized Robust matrix Completion for the Community Detection Problem

  2018cites this paper
• A PAC-Theory of Clustering with Advice

  2018cites this paper
• Fast Landform Position Classification to Improve the Accuracy of Remote Sensing Land Cover Mapping

  2018cites this paper
• Scalable and Robust Community Detection With Randomized Sketching

  2018cites this paper
• Uniform Deviation Bounds for Unbounded Loss Functions like k-Means

  2017cites this paper
• Uniform Deviation Bounds for k-Means Clustering

  2017cites this paper
• An Intelligent Decision System for Intraoperative Somatosensory Evoked Potential Monitoring

  2016cites this paper
• Fast learning rates with heavy-tailed losses

  2016cites this paper
• Computational Feasibility of Clustering under Clusterability Assumptions

  2015cites this paper
• Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning

  2015cites this paper
• Representation Learning for Clustering: A Statistical Framework

  2015influential citation
• Data Driven Resource Allocation for Distributed Learning

  2015cites this paper
• Feature selection for text clustering in limited memory using Monte Carlo wrapper

  2014cites this paper
• Clustered Support Vector Machines

  2013cites this paper
• Learning Manifolds with K-Means and K-Flats

  2012cites this paper
• Active Clustering of Biological Sequences

  2012cites this paper
• Stratified k-means clustering over a deep web data source

  2012cites this paper
• Sample Complexity in Manifold Learning

  2011cites this paper
• Clustering and network analysis with biological applications

  2011cites this paper
• Clustering and Network Analysis with Biological Applications Dissertation Clustering and Network Analysis with Biological Applications Assistant Professor of Chemistry Third Reader Clustering and Network Analysis with Biological Applications (order No. )

  2011cites this paper
• Bandits Games and Clustering Foundations

  2010cites this paper
• Sample Complexity of Testing the Manifold Hypothesis

  2010cites this paper
• MMSVC: An efficient unsupervised learning approach for large-scale datasets

  2010cites this paper
• Efficient Clustering with Limited Distance Information

  2010cites this paper
• Clustering Stability: An Overview

  2010cites this paper
• Contributions to Unsupervised and Semi-Supervised Learning

  2009influential citation
• Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions

  2009cites this paper
• Approximation Algorithms for Tensor Clustering

  2009cites this paper
• Relating Clustering Stability to Properties of Cluster Boundaries

  2008influential citation
• A discriminative framework for clustering via similarity functions

  2008cites this paper
• A Learning Theoretic Framework for Clustering with Similarity Functions

  2007cites this paper
• A Theoretical Study of Clusterability and Clustering Quality

  2007cites this paper